COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

Abstract

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

Keywords: Chest X-ray images; Chest radiography imaging; Coronavirus COVID-19 epidemic; Deep learning; ResNet34 model; Transfer learning.

Fig. 1

The plots of the number of newly infected versus the number of recovered and discharged patients each day (Worldmeter 2020)

Fig. 2

Block diagram of the proposed COVID-CheXNet system for diagnosing COVID-19 pneumonia in chest X-rays images

Fig. 3

Some samples of normal and COVID-19 infected cases from the created COVID19-vs-normal dataset

Fig. 4

Proposed image enhancement procedure outputs: a A raw X-ray image, b applying the CLAHE method, and c applying the Butterworth bandpass filter

Fig. 5

The difference between: a regular block and b residual block

Fig. 6

The main architecture of the HRNet model

Fig. 7

The curve of the loss against the log scales of the learning rates, to find a perfect order of magnitude of the learning rate

Fig. 8

The curve of the loss against the batches processed in the training and validation sets: a ResNet34 model, and b HRNet model

Fig. 9

ROC curves for the proposed deep learning models trained on two different datasets: a ResNet34 model trained using raw images, b HRNet model trained using raw images, c ResNet34 model trained using pre-processing X-ray images, and d, c HRNet model trained using pre-processing X-ray images

Fig. 10

Confusion matrices for the proposed deep learning models trained on two different datasets: a ResNet34 model trained using raw images, b HRNet model trained using raw images, c ResNet34 model trained using pre-processing X-ray images, and d, c HRNet model trained using pre-processing X-ray images

Fig. 11

Confusion matrices for the proposed COVID-CheXNet system using different fusion rules: a WSR rule in the score-level fusion, and b OR rule in the decision-level fusion